Scikit Learn

Scikit-learn is a machine learning library for Python. It features several regression, classification and clustering algorithms including SVMs, gradient boosting, k-means, random forests and DBSCAN. It is designed to work with Python Numpy and SciPy.

The scikit-learn project kicked off as a Google Summer of Code (also known as GSoC) project by David Cournapeau as scikits.learn. It gets its name from “Scikit”, a separate third-party extension to SciPy.

Python Scikit-learn

Scikit is written in Python (most of it) and some of its core algorithms are written in Cython for even better performance.

Scikit-learn is used to build models and it is not recommended to use it for reading, manipulating and summarizing data as there are better frameworks available for the purpose.

It is open source and released under BSD license.

Install Scikit Learn

Scikit assumes you have a running Python 2.7 or above platform with NumPY (1.8.2 and above) and SciPY (0.13.3 and above) packages on your device. Once we have these packages installed we can proceed with the installation.

If you like conda, you can also use the conda for package installation, run the following command:

conda install scikit-learn

Using Scikit-Learn

Once you are done with the installation, you can use scikit-learn easily in your Python code by importing it as:

import sklearn

Scikit Learn Loading Dataset

Let’s start with loading a dataset to play with. Let’s load a simple dataset named Iris. It is a dataset of a flower, it contains 150 observations about different measurements of the flower. Let’s see how to load the dataset using scikit-learn.

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